{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T01:17:03Z","timestamp":1783732623015,"version":"3.55.0"},"reference-count":22,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2025,2,25]],"date-time":"2025-02-25T00:00:00Z","timestamp":1740441600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,2,25]],"date-time":"2025-02-25T00:00:00Z","timestamp":1740441600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62172351"],"award-info":[{"award-number":["62172351"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2024MF075"],"award-info":[{"award-number":["ZR2024MF075"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cluster Comput"],"published-print":{"date-parts":[[2025,8]]},"DOI":"10.1007\/s10586-024-04980-9","type":"journal-article","created":{"date-parts":[[2025,2,25]],"date-time":"2025-02-25T14:05:46Z","timestamp":1740492346000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["SMOTE oversampling algorithm based on generative adversarial network"],"prefix":"10.1007","volume":"28","author":[{"given":"Yu","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qicheng","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,2,25]]},"reference":[{"issue":"8","key":"4980_CR1","doi-asserted-by":"publisher","first-page":"3320","DOI":"10.1109\/TNNLS.2021.3051721","volume":"33","author":"L Wang","year":"2022","unstructured":"Wang, L., Zhang, L., Yi, Z.: Deep attention-based imbalanced image classification. Trans. Neural Networks Learn. Syst. 33(8), 3320\u20133330 (2022)","journal-title":"Trans. Neural Networks Learn. Syst."},{"issue":"1","key":"4980_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-019-0192-5","volume":"6","author":"JM Johnson","year":"2019","unstructured":"Johnson, J.M., Khoshgoftaar, T.M.: Survey on deep learning with class imbalance. J. Big Data 6(1), 1\u201354 (2019)","journal-title":"J. Big Data"},{"key":"4980_CR3","doi-asserted-by":"crossref","unstructured":"Cho, H.-Y., Kim, Y.-H.: A genetic algorithm to optimize smote and gan ratios in class imbalanced datasets. In: Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion, pp. 33\u201334 (2020)","DOI":"10.1145\/3377929.3398153"},{"key":"4980_CR4","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. Adv. Neural Inform. Proc. Syst. 27 (2014)"},{"key":"4980_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12938-018-0604-3","volume":"17","author":"L Zhang","year":"2018","unstructured":"Zhang, L., Yang, H., Jiang, Z.: Imbalanced biomedical data classification using self-adaptive multilayer ELM combined with dynamic GAN. Biomed. Eng. Online 17, 1\u201321 (2018)","journal-title":"Biomed. Eng. Online"},{"key":"4980_CR6","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: Smote: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321\u2013357 (2002)","journal-title":"J. Artif. Intell. Res."},{"key":"4980_CR7","doi-asserted-by":"publisher","first-page":"110235","DOI":"10.1016\/j.knosys.2022.110235","volume":"262","author":"K El Moutaouakil","year":"2023","unstructured":"El Moutaouakil, K., Roudani, M., El Ouissari, A.: Optimal entropy genetic fuzzy-C-means smote (OEGFCM-SMOTE). Knowl.-Based Syst. 262, 110235 (2023)","journal-title":"Knowl.-Based Syst."},{"issue":"8","key":"4980_CR8","doi-asserted-by":"publisher","first-page":"5059","DOI":"10.1016\/j.jksuci.2022.06.005","volume":"34","author":"A Arafa","year":"2022","unstructured":"Arafa, A., El-Fishawy, N., Badawy, M., Radad, M.: Rn-smote: reduced noise smote based on dbscan for enhancing imbalanced data classification. J. King Saud Univ.-Comput. Inform. Sci. 34(8), 5059\u20135074 (2022)","journal-title":"J. King Saud Univ.-Comput. Inform. Sci."},{"issue":"4","key":"4980_CR9","doi-asserted-by":"publisher","first-page":"3205","DOI":"10.1007\/s13369-019-04336-1","volume":"45","author":"H-A Majzoub","year":"2020","unstructured":"Majzoub, H.-A., Elgedawy, I., et al.: Hcab-smote: a hybrid clustered affinitive borderline smote approach for imbalanced data binary classification. Arabian J. Sci. Eng. 45(4), 3205\u20133222 (2020)","journal-title":"Arabian J. Sci. Eng."},{"key":"4980_CR10","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1016\/j.ins.2019.06.007","volume":"501","author":"G Douzas","year":"2019","unstructured":"Douzas, G., Bacao, F.: Geometric smote a geometrically enhanced drop-in replacement for smote. Inform. Sci. 501, 118\u2013135 (2019)","journal-title":"Inform. Sci."},{"issue":"14","key":"4980_CR11","doi-asserted-by":"publisher","first-page":"5166","DOI":"10.3390\/s22145166","volume":"22","author":"F Duan","year":"2022","unstructured":"Duan, F., Zhang, S., Yan, Y., Cai, Z.: An oversampling method of unbalanced data for mechanical fault diagnosis based on meanradius-smote. Sensors (Basel) 22(14), 5166 (2022)","journal-title":"Sensors (Basel)"},{"issue":"9","key":"4980_CR12","first-page":"273","volume":"38","author":"Z Tianyi","year":"2021","unstructured":"Tianyi, Z., Lixin, D.: A smote-based resampling method for imbalanced datasets. Comput. Appl. Software 38(9), 273\u2013279 (2021)","journal-title":"Comput. Appl. Software"},{"key":"4980_CR13","doi-asserted-by":"publisher","first-page":"91452","DOI":"10.1109\/ACCESS.2020.3018911","volume":"10","author":"C Liu","year":"2020","unstructured":"Liu, C., Jin, S., Wang, D., Luo, Z., Yu, J., Zhou, B., Yang, C.: Constrained oversampling: an oversampling approach to reduce noise generation in imbalanced datasets with class overlapping. IEEE Access 10, 91452\u201391465 (2020)","journal-title":"IEEE Access"},{"issue":"5","key":"4980_CR14","doi-asserted-by":"publisher","first-page":"2100031","DOI":"10.1002\/adts.202100031","volume":"4","author":"Z Jiang","year":"2021","unstructured":"Jiang, Z., Yang, J., Liu, Y.: Imbalanced learning with oversampling based on classification contribution degree. Adv. Theory Simul. 4(5), 2100031 (2021)","journal-title":"Adv. Theory Simul."},{"issue":"3","key":"4980_CR15","doi-asserted-by":"publisher","first-page":"773","DOI":"10.1007\/s13042-022-01662-z","volume":"14","author":"J Zhang","year":"2023","unstructured":"Zhang, J., Wang, T., Ng, W.W., Pedrycz, W.: Perturbation-based oversampling technique for imbalanced classification problems. Int. J. Mach. Learn. Cybern. 14(3), 773\u2013787 (2023)","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"4980_CR16","doi-asserted-by":"crossref","unstructured":"Ai-jun, L., Peng, Z.: Research on unbalanced data processing algorithm base tomeklinks-smote. In: Proceedings of the 2020 3rd International Conference on Artificial Intelligence and Pattern Recognition, pp. 13\u201317 (2020)","DOI":"10.1145\/3430199.3430222"},{"key":"4980_CR17","doi-asserted-by":"publisher","first-page":"111659","DOI":"10.1016\/j.asoc.2024.111659","volume":"159","author":"X Yuan","year":"2024","unstructured":"Yuan, X., Sun, C., Chen, S.: A clustering-based adaptive undersampling ensemble method for highly unbalanced data classification. Appl. Soft Comput. 159, 111659 (2024)","journal-title":"Appl. Soft Comput."},{"key":"4980_CR18","doi-asserted-by":"publisher","first-page":"121848","DOI":"10.1016\/j.eswa.2023.121848","volume":"238","author":"P Sun","year":"2024","unstructured":"Sun, P., Wang, Z., Jia, L., Xu, Z.: Smote-ktlnn: a hybrid re-sampling method based on smote and a two-layer nearest neighbor classifier. Expert Syst. Appl. 238, 121848 (2024)","journal-title":"Expert Syst. Appl."},{"key":"4980_CR19","doi-asserted-by":"crossref","unstructured":"Alamri, M., Ykhlef, M.: Hybrid undersampling and oversampling for handling imbalanced credit card data. IEEE Access (2024)","DOI":"10.1109\/ACCESS.2024.3357091"},{"key":"4980_CR20","doi-asserted-by":"publisher","first-page":"30655","DOI":"10.1109\/ACCESS.2022.3158977","volume":"10","author":"A Sharma","year":"2022","unstructured":"Sharma, A., Singh, P.K., Chandra, R.: Smotified-gan for class imbalanced pattern classification problems. IEEE Access 10, 30655\u201330665 (2022)","journal-title":"IEEE Access"},{"key":"4980_CR21","doi-asserted-by":"publisher","first-page":"114582","DOI":"10.1016\/j.eswa.2021.114582","volume":"174","author":"J Engelmann","year":"2021","unstructured":"Engelmann, J., Lessmann, S.: Conditional wasserstein gan-based oversampling of tabular data for imbalanced learning. Expert Syst. Appl. 174, 114582 (2021)","journal-title":"Expert Syst. Appl."},{"issue":"9","key":"4980_CR22","doi-asserted-by":"publisher","first-page":"6390","DOI":"10.1109\/TNNLS.2021.3136503","volume":"34","author":"D Dablain","year":"2022","unstructured":"Dablain, D., Krawczyk, B., Chawla, N.V.: Deepsmote: fusing deep learning and smote for imbalanced data. IEEE Trans. Neural Networks Learn. Syst. 34(9), 6390\u20136404 (2022)","journal-title":"IEEE Trans. Neural Networks Learn. Syst."}],"container-title":["Cluster Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-024-04980-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10586-024-04980-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-024-04980-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T06:34:26Z","timestamp":1757140466000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10586-024-04980-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,25]]},"references-count":22,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,8]]}},"alternative-id":["4980"],"URL":"https:\/\/doi.org\/10.1007\/s10586-024-04980-9","relation":{},"ISSN":["1386-7857","1573-7543"],"issn-type":[{"value":"1386-7857","type":"print"},{"value":"1573-7543","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,25]]},"assertion":[{"value":"24 July 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 November 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 December 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 February 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no relevant financial or non-financial interests to disclose.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}],"article-number":"271"}}